Overview

Dataset statistics

Number of variables12
Number of observations3961
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.3 KiB
Average record size in memory104.0 B

Variable types

Numeric11
Text1

Alerts

free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation

Reproduction

Analysis started2025-02-21 12:39:59.854475
Analysis finished2025-02-21 12:40:19.799740
Duration19.95 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct68
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8393461
Minimum3.8
Maximum14.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:19.944118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.3
median6.8
Q37.3
95-th percentile8.3
Maximum14.2
Range10.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86685974
Coefficient of variation (CV)0.126746
Kurtosis2.2530474
Mean6.8393461
Median Absolute Deviation (MAD)0.5
Skewness0.69610022
Sum27090.65
Variance0.75144581
MonotonicityNot monotonic
2025-02-21T18:10:20.149758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 241
 
6.1%
6.6 238
 
6.0%
6.4 224
 
5.7%
6.9 191
 
4.8%
6.7 190
 
4.8%
6.5 182
 
4.6%
7 179
 
4.5%
6.2 159
 
4.0%
6.3 158
 
4.0%
7.1 154
 
3.9%
Other values (58) 2045
51.6%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 1
 
< 0.1%
4.7 5
 
0.1%
4.8 9
0.2%
4.9 5
 
0.1%
5 20
0.5%
ValueCountFrequency (%)
14.2 1
 
< 0.1%
11.8 1
 
< 0.1%
10.7 1
 
< 0.1%
10.3 2
 
0.1%
10.2 1
 
< 0.1%
10 3
 
0.1%
9.9 2
 
0.1%
9.8 8
0.2%
9.7 3
 
0.1%
9.6 5
0.1%

volatile acidity
Real number (ℝ)

Distinct125
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.559335
Minimum0.08
Maximum965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:20.351348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.21
median0.27
Q30.33
95-th percentile0.58
Maximum965
Range964.92
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation72.603482
Coefficient of variation (CV)5.7808382
Kurtosis55.575696
Mean12.559335
Median Absolute Deviation (MAD)0.06
Skewness7.0191599
Sum49747.525
Variance5271.2656
MonotonicityNot monotonic
2025-02-21T18:10:20.538038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 213
 
5.4%
0.24 208
 
5.3%
0.26 207
 
5.2%
0.25 179
 
4.5%
0.22 178
 
4.5%
0.27 175
 
4.4%
0.2 175
 
4.4%
0.23 173
 
4.4%
0.21 158
 
4.0%
0.3 154
 
3.9%
Other values (115) 2141
54.1%
ValueCountFrequency (%)
0.08 2
 
0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.2%
0.11 9
 
0.2%
0.12 28
 
0.7%
0.13 36
 
0.9%
0.14 48
1.2%
0.15 67
1.7%
0.16 109
2.8%
0.17 113
2.9%
ValueCountFrequency (%)
965 1
 
< 0.1%
905 1
 
< 0.1%
815 1
 
< 0.1%
785 1
 
< 0.1%
705 2
0.1%
695 3
0.1%
685 1
 
< 0.1%
655 3
0.1%
615 3
0.1%
595 2
0.1%

citric acid
Real number (ℝ)

Distinct87
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33433224
Minimum0
Maximum1.66
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:20.769174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.27
median0.32
Q30.39
95-th percentile0.53
Maximum1.66
Range1.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.12244609
Coefficient of variation (CV)0.36624075
Kurtosis6.8448082
Mean0.33433224
Median Absolute Deviation (MAD)0.06
Skewness1.310601
Sum1324.29
Variance0.014993045
MonotonicityNot monotonic
2025-02-21T18:10:20.979599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 239
 
6.0%
0.28 220
 
5.6%
0.32 214
 
5.4%
0.34 181
 
4.6%
0.29 179
 
4.5%
0.49 173
 
4.4%
0.26 173
 
4.4%
0.27 164
 
4.1%
0.31 162
 
4.1%
0.33 155
 
3.9%
Other values (77) 2101
53.0%
ValueCountFrequency (%)
0 18
0.5%
0.01 6
 
0.2%
0.02 6
 
0.2%
0.03 2
 
0.1%
0.04 10
0.3%
0.05 5
 
0.1%
0.06 5
 
0.1%
0.07 11
0.3%
0.08 4
 
0.1%
0.09 11
0.3%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 5
0.1%
0.99 1
 
< 0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
0.1%
0.81 2
 
0.1%
0.8 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct310
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9148195
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:21.169838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.6
median4.7
Q38.9
95-th percentile15.2
Maximum65.8
Range65.2
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation4.8616463
Coefficient of variation (CV)0.82194331
Kurtosis5.6815122
Mean5.9148195
Median Absolute Deviation (MAD)3.2
Skewness1.333639
Sum23428.6
Variance23.635605
MonotonicityNot monotonic
2025-02-21T18:10:21.386030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4 165
 
4.2%
1.2 165
 
4.2%
1.6 144
 
3.6%
1.3 134
 
3.4%
1.1 126
 
3.2%
1.5 125
 
3.2%
1.7 87
 
2.2%
1.8 85
 
2.1%
1 77
 
1.9%
2 67
 
1.7%
Other values (300) 2786
70.3%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 7
 
0.2%
0.8 25
 
0.6%
0.9 35
 
0.9%
0.95 3
 
0.1%
1 77
1.9%
1.05 1
 
< 0.1%
1.1 126
3.2%
1.15 3
 
0.1%
1.2 165
4.2%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 1
< 0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 1
< 0.1%
20.8 2
0.1%
20.7 1
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

Distinct160
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.618773
Minimum0.02
Maximum346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:21.595310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.04
Q132
median41
Q348
95-th percentile66
Maximum346
Range345.98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation25.930754
Coefficient of variation (CV)0.63839334
Kurtosis20.682455
Mean40.618773
Median Absolute Deviation (MAD)8
Skewness2.9656102
Sum160890.96
Variance672.40401
MonotonicityNot monotonic
2025-02-21T18:10:21.787645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 165
 
4.2%
44 155
 
3.9%
42 155
 
3.9%
0.04 152
 
3.8%
46 152
 
3.8%
47 145
 
3.7%
38 140
 
3.5%
34 137
 
3.5%
37 136
 
3.4%
48 135
 
3.4%
Other values (150) 2489
62.8%
ValueCountFrequency (%)
0.02 13
 
0.3%
0.03 93
2.3%
0.04 152
3.8%
0.05 130
3.3%
0.06 37
 
0.9%
0.07 6
 
0.2%
0.08 4
 
0.1%
0.09 2
 
0.1%
0.11 2
 
0.1%
0.12 1
 
< 0.1%
ValueCountFrequency (%)
346 1
< 0.1%
301 1
< 0.1%
271 1
< 0.1%
255 1
< 0.1%
244 1
< 0.1%
239 1
< 0.1%
217 1
< 0.1%
212 1
< 0.1%
211 1
< 0.1%
209 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.889169
Minimum2
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:21.977628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q123
median33
Q345
95-th percentile63
Maximum289
Range287
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.210021
Coefficient of variation (CV)0.49327688
Kurtosis13.434025
Mean34.889169
Median Absolute Deviation (MAD)11
Skewness1.5666802
Sum138196
Variance296.18481
MonotonicityNot monotonic
2025-02-21T18:10:22.183407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 125
 
3.2%
31 110
 
2.8%
34 107
 
2.7%
26 105
 
2.7%
36 102
 
2.6%
24 101
 
2.5%
35 97
 
2.4%
28 95
 
2.4%
23 93
 
2.3%
25 92
 
2.3%
Other values (122) 2934
74.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 9
 
0.2%
4 9
 
0.2%
5 23
0.6%
6 29
0.7%
7 21
0.5%
8 29
0.7%
9 26
0.7%
10 44
1.1%
11 38
1.0%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.19351
Minimum9
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:22.372423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile73
Q1106
median133
Q3166
95-th percentile212
Maximum440
Range431
Interquartile range (IQR)60

Descriptive statistics

Standard deviation43.129065
Coefficient of variation (CV)0.31436665
Kurtosis0.73525786
Mean137.19351
Median Absolute Deviation (MAD)29
Skewness0.45679968
Sum543423.5
Variance1860.1163
MonotonicityNot monotonic
2025-02-21T18:10:22.572890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 51
 
1.3%
114 47
 
1.2%
113 46
 
1.2%
128 45
 
1.1%
122 45
 
1.1%
117 44
 
1.1%
150 43
 
1.1%
126 42
 
1.1%
98 41
 
1.0%
118 41
 
1.0%
Other values (241) 3516
88.8%
ValueCountFrequency (%)
9 1
 
< 0.1%
10 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
24 2
0.1%
25 1
 
< 0.1%
26 1
 
< 0.1%
28 3
0.1%
29 2
0.1%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
282 1
< 0.1%
272 2
0.1%
260 1
< 0.1%

density
Real number (ℝ)

Distinct890
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.60818
Minimum0.99
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:22.755982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile9.906
Q19.939
median98.935
Q399.374
95-th percentile992
Maximum999
Range998.01
Interquartile range (IQR)89.435

Descriptive statistics

Standard deviation239.35109
Coefficient of variation (CV)2.0526098
Kurtosis9.1548744
Mean116.60818
Median Absolute Deviation (MAD)88.957
Skewness3.2656309
Sum461884.99
Variance57288.943
MonotonicityNot monotonic
2025-02-21T18:10:22.963523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
992 60
 
1.5%
9.928 52
 
1.3%
9.932 49
 
1.2%
993 46
 
1.2%
9.934 46
 
1.2%
9.938 43
 
1.1%
9.944 41
 
1.0%
9.927 40
 
1.0%
9.924 39
 
1.0%
9.954 37
 
0.9%
Other values (880) 3508
88.6%
ValueCountFrequency (%)
0.99 25
0.6%
1 13
0.3%
1.001 3
 
0.1%
1.0103 1
 
< 0.1%
1.2001 5
 
0.1%
1.2002 6
 
0.2%
1.2003 1
 
< 0.1%
1.2004 6
 
0.2%
1.2005 2
 
0.1%
1.2006 3
 
0.1%
ValueCountFrequency (%)
999 5
 
0.1%
998.365 1
 
< 0.1%
998.275 1
 
< 0.1%
998 25
0.6%
997 20
 
0.5%
996 15
 
0.4%
995 22
 
0.6%
994 34
0.9%
993 46
1.2%
992 60
1.5%

pH
Real number (ℝ)

Distinct103
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1954582
Minimum2.72
Maximum3.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:23.201217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.96
Q13.09
median3.18
Q33.29
95-th percentile3.46
Maximum3.82
Range1.1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.15154557
Coefficient of variation (CV)0.047425301
Kurtosis0.54995703
Mean3.1954582
Median Absolute Deviation (MAD)0.1
Skewness0.45545683
Sum12657.21
Variance0.022966059
MonotonicityNot monotonic
2025-02-21T18:10:23.403987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 128
 
3.2%
3.14 127
 
3.2%
3.22 119
 
3.0%
3.19 115
 
2.9%
3.24 114
 
2.9%
3.18 114
 
2.9%
3.15 114
 
2.9%
3.2 111
 
2.8%
3.12 111
 
2.8%
3.1 109
 
2.8%
Other values (93) 2799
70.7%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 1
 
< 0.1%
2.77 1
 
< 0.1%
2.79 2
 
0.1%
2.8 3
0.1%
2.82 1
 
< 0.1%
2.83 3
0.1%
2.84 1
 
< 0.1%
2.85 6
0.2%
2.86 7
0.2%
ValueCountFrequency (%)
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
0.1%
3.79 1
< 0.1%
3.77 2
0.1%
3.76 2
0.1%
3.75 2
0.1%
3.74 2
0.1%
3.72 2
0.1%
3.7 1
< 0.1%

sulphates
Real number (ℝ)

Distinct79
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49035092
Minimum0.22
Maximum1.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:23.642839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.34
Q10.41
median0.48
Q30.55
95-th percentile0.7
Maximum1.08
Range0.86
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.11352281
Coefficient of variation (CV)0.23151339
Kurtosis1.5650206
Mean0.49035092
Median Absolute Deviation (MAD)0.07
Skewness0.93785334
Sum1942.28
Variance0.012887427
MonotonicityNot monotonic
2025-02-21T18:10:23.834794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 191
 
4.8%
0.46 182
 
4.6%
0.44 171
 
4.3%
0.38 165
 
4.2%
0.45 148
 
3.7%
0.47 144
 
3.6%
0.42 144
 
3.6%
0.48 142
 
3.6%
0.54 136
 
3.4%
0.49 134
 
3.4%
Other values (69) 2404
60.7%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 3
 
0.1%
0.27 10
 
0.3%
0.28 12
 
0.3%
0.29 12
 
0.3%
0.3 24
0.6%
0.31 31
0.8%
0.32 44
1.1%
ValueCountFrequency (%)
1.08 1
 
< 0.1%
1.06 1
 
< 0.1%
1.01 1
 
< 0.1%
1 1
 
< 0.1%
0.99 1
 
< 0.1%
0.98 3
0.1%
0.97 1
 
< 0.1%
0.96 3
0.1%
0.95 3
0.1%
0.94 2
0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8548346
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:23.976864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89068268
Coefficient of variation (CV)0.15212773
Kurtosis0.29934517
Mean5.8548346
Median Absolute Deviation (MAD)1
Skewness0.11200403
Sum23191
Variance0.79331564
MonotonicityNot monotonic
2025-02-21T18:10:24.103359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1788
45.1%
5 1175
29.7%
7 689
 
17.4%
4 153
 
3.9%
8 131
 
3.3%
3 20
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 20
 
0.5%
4 153
 
3.9%
5 1175
29.7%
6 1788
45.1%
7 689
 
17.4%
8 131
 
3.3%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 131
 
3.3%
7 689
 
17.4%
6 1788
45.1%
5 1175
29.7%
4 153
 
3.9%
3 20
 
0.5%
Distinct101
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
2025-02-21T18:10:24.404484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length25
Median length12
Mean length11.49028
Min length3

Characters and Unicode

Total characters45513
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)0.8%

Sample

1st rowR$ 45.512,00
2nd rowR$ 45.421,00
3rd rowR$ 45.301,00
4th rowR$ 45.544,00
5th rowR$ 45.452,00
ValueCountFrequency (%)
r 3933
49.8%
45.421,00 177
 
2.2%
45.391,00 169
 
2.1%
10,00 143
 
1.8%
45.422,00 140
 
1.8%
45.331,00 140
 
1.8%
11,00 130
 
1.6%
45.392,00 130
 
1.6%
45.514,00 119
 
1.5%
9,00 116
 
1.5%
Other values (92) 2697
34.2%
2025-02-21T18:10:24.962084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8594
18.9%
4 5352
11.8%
5 4715
10.4%
R 3933
8.6%
$ 3933
8.6%
3933
8.6%
, 3933
8.6%
. 3529
7.8%
3 3008
 
6.6%
1 1712
 
3.8%
Other values (5) 2871
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8594
18.9%
4 5352
11.8%
5 4715
10.4%
R 3933
8.6%
$ 3933
8.6%
3933
8.6%
, 3933
8.6%
. 3529
7.8%
3 3008
 
6.6%
1 1712
 
3.8%
Other values (5) 2871
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8594
18.9%
4 5352
11.8%
5 4715
10.4%
R 3933
8.6%
$ 3933
8.6%
3933
8.6%
, 3933
8.6%
. 3529
7.8%
3 3008
 
6.6%
1 1712
 
3.8%
Other values (5) 2871
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8594
18.9%
4 5352
11.8%
5 4715
10.4%
R 3933
8.6%
$ 3933
8.6%
3933
8.6%
, 3933
8.6%
. 3529
7.8%
3 3008
 
6.6%
1 1712
 
3.8%
Other values (5) 2871
 
6.3%

Interactions

2025-02-21T18:10:17.744639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:00.252195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.830960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.491960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.258052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.876147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.475637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.527776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.140026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.702154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.226295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.878247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:00.422106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.965293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.644508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.402174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.022436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.609646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.661987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.274838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.826432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.362115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.021982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:00.583992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.104176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.776270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.541979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.158797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.757148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.792952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.424487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.975633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.508559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.165385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:00.737314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.279257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.948953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.703261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.305025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:10.264698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.958302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.560478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.109649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.645299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.309308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:00.887693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.424921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.109172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.872955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.445657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:10.472997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.108909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.710440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.259214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.776048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.465438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.040364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.574800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.271054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.009017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.585103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:10.646794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.260939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.858687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.392121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.924442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.615248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.201353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.726071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.420235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.158958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.738272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:10.792930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.408312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:13.996675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.526225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.065025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.765567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.313926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:02.924602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.575541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.297112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:07.872997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:10.925545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.528251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.142171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.661979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.191927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:18.911977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.455051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.060402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.745259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.447461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.017787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.059747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.687753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.279946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.824312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.328763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:19.045334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.570334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.215208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:04.897867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.593558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.167097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.239710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.825616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.419252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:15.971591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.469661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:19.178630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:01.696818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:03.352197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:05.102879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:06.735929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:08.331400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:11.359399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:12.969290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:14.553143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:16.097979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-21T18:10:17.596285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-02-21T18:10:25.104515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
chloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
chlorides1.0000.0430.0870.0590.149-0.019-0.2510.1530.0850.3020.016
citric acid0.0431.000-0.0870.3090.084-0.1690.0300.0310.0630.090-0.169
density0.087-0.0871.000-0.0270.107-0.088-0.1210.2860.0980.1200.011
fixed acidity0.0590.309-0.0271.000-0.036-0.420-0.0940.092-0.0080.096-0.073
free sulfur dioxide0.1490.0840.107-0.0361.000-0.0170.0330.3450.0320.620-0.087
pH-0.019-0.169-0.088-0.420-0.0171.0000.136-0.1530.131-0.002-0.038
quality-0.2510.030-0.121-0.0940.0330.1361.000-0.0920.036-0.203-0.170
residual sugar0.1530.0310.2860.0920.345-0.153-0.0921.0000.0010.4320.128
sulphates0.0850.0630.098-0.0080.0320.1310.0360.0011.0000.157-0.015
total sulfur dioxide0.3020.0900.1200.0960.620-0.002-0.2030.4320.1571.0000.115
volatile acidity0.016-0.1690.011-0.073-0.087-0.038-0.1700.128-0.0150.1151.000

Missing values

2025-02-21T18:10:19.384629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-21T18:10:19.651499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesqualityalcohol
07.00.270.3620.7045.0045.0170.01.0013.000.456R$ 45.512,00
16.30.300.341.6049.0014.0132.0994.0003.300.496R$ 45.421,00
28.10.280.406.900.0530.097.09.9513.260.446R$ 45.301,00
37.20.230.328.5058.0047.0186.09.9563.190.406R$ 45.544,00
66.20.320.167.0045.0030.0136.09.9493.180.476R$ 45.452,00
98.10.220.431.5044.0028.0129.09.9383.220.456R$ 11,00
108.10.270.411.4533.0011.063.09.9082.990.565R$ 12,00
118.60.230.404.2035.0017.0109.09.9473.140.535R$ 45.482,00
127.90.180.371.200.0416.075.0992.0003.180.635R$ 45.514,00
136.60.160.401.5044.0048.0143.09.9123.540.527R$ 45.394,00
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesqualityalcohol
48886.80.220.361.2052.0038.0127.09.9333.040.545R$ 45.331,00
48894.9235.000.2711.750.0334.0118.09.9543.070.506R$ 45.391,00
48906.10.340.292.2036.0025.0100.098.9383.060.446R$ 45.515,00
48915.70.210.320.9038.0038.0121.099.0743.240.466R$ 45.453,00
48926.50.230.381.3032.0029.0112.099.2983.290.545R$ 45.482,00
48936.20.210.291.6039.0024.092.099.1143.270.506R$ 45.333,00
48946.60.320.368.0047.0057.0168.09.9493.150.465R$ 45.452,00
48956.50.240.191.2041.0030.0111.099.2542.990.466R$ 45.391,00
48965.50.290.301.1022.0020.0110.098.8693.340.387R$ 45.516,00
48976.00.210.380.800.0222.098.098.9413.260.326R$ 45.515,00